Edge devices such as mobile phones and IoT devices have become the primary computing platform today.
These devices generate a tremendous amount of valuable data while providing hidden insights about the human world. In this case, AI can hugely help evaluate the data and make sense of it.
By training machine learning models that simulate intelligent behaviour to make well-informed decisions with little or no human intervention.
However, analyzing large amounts of data with complex machine learning algorithms requires significant computational capabilities.
Thus, users have to upload their data to a cloud server to train a satisfactory model, which is undesirable from a privacy, security, regulatory or economic perspective.
As an emerging and promising distributed machine learning paradigm, federated learning (FL) allows multiple devices to jointly train a shared model without direct access to sensitive training data [1]}, [2]}.
In FL, devices train on an initial model locally with their own data, and upload their model updates to the federated server to build a shared global model [3]}, [4]}.
However, in dynamic edge environments, data distribution is often not as static. To allow intelligent applications to adapt to the current environment with limited resources consumption, we need to eliminate specific categories of data from the trained models.
Moreover, recent privacy legislation such as the European Union's General Data Protection Regulation (GDPR) and the former Right to be Forgotten [5]} also give users the right to eliminate specific categories of data from the trained model.
These practical needs call for efficient techniques that enable FL models to unlearn, or to forget what has been learned from the categories to be removed (referred to as the target categories).
